Image searches involve the challenge of matching text in a search request with text associated with the image, e.g., tags and so forth. For example, a creative professional may capture an image and associate tags having text that are used to locate the image. On the other side, a user trying to locate the image in an image search enters one or more keywords. Accordingly, this requires that the creative professional and the users reach agreement as to how to describe the image using text in order for the user to locate the image and for the creative professional to make the image available to user's that desire the image. As such, conventional tag and keyword search techniques may be prone to error, misunderstandings, and different interpretations thus leading to inaccurate search results.
Further, conventional search techniques for images do not support high precision semantic image search due to limitations of conventional image tagging and search. This is because conventional techniques merely associate tags with the images, but do not define relationships between the tags nor with the image itself. As such, conventional search techniques cannot achieve accurate search results for complex search queries, such as a “man feeding a baby in a high chair with the baby holding a toy.” Consequently, these conventional search techniques force users to navigate through tens, hundreds, and even thousands of images oftentimes using multiple search requests in order to locate an image of interest.